Copula-Based Fuzzy Clustering of Count Data with Total Variation Distance

This paper presents a novel fuzzy clustering technique designed specifically for count data, referred to as the Fuzzy C-medoids algorithm based on Total Variation Distance. We evaluate its performance against a benchmark relying on Shannon divergence, commonly employed in scenarios involving discrete probability distributions, through simulation analysis. A comprehensive evaluation of the proposed approach's effectiveness is carried out, revealing promising results. The study's findings emphasize the potential of the proposed fuzzy method, particularly in scenarios where discrete probability distributions are involved.